Many practical situations can be modeled using random variables that are governed by specific laws. It is therefore important to study probabilistic models that can later allow us to analyze the fluctuations of certain phenomena by evaluating, for example, the probabilities that a particular event or outcome will be observed. In the area of statistics, normal distributions are among the most widely used probability distributions for modeling natural experiments involving multiple random occurrences [1]}, [2]}. They are also called Gaussian distributions [3]}, [4]}. More formally, a normal distribution is an absolutely continuous probability law and depends on two unknown parameters, namely the mathematical expectation which is a real number denoted \(\mu \) , and its standard deviation or variance denoted \(\sigma \) . The probability distribution function of the normal law of expectation \(\mu \)  and standard deviation \(\sigma \)  is provided by
\(p\left(x; \theta \right)=\frac{1}{\sqrt{2\pi }\sigma }\exp \left[-\frac{\left(x-\mu \right)^{2}}{2\sigma ^{2}}\right]\equiv \frac{1}{\sqrt{2\pi \theta _{2}}}\exp \left[-\frac{\left(x-\theta _{1}\right)^{2}}{2\theta _{2}}\right].\) 
